Source Data Verification by Statistical Sampling: Issues in Implementation
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Source Data Verification by Statistical Sampling: Issues in Implementation
Drug Information Journal 46(3) 368-377 ª The Author(s) 2012 Reprints and permission: sagepub.com/journalsPermissions.nav DOI: 10.1177/0092861512442057 http://dij.sagepub.com
Andrew P. Grieve, BSc, MSc, PhD, DSc1
Abstract Efficiency of the drug development process is a continuing concern for pharmaceutical companies, governments, regulatory authorities, and patients. While much time and effort have been spent on developments in genetics and on sophisticated statistical designs, there has been less concern about the processes that govern the running of clinical trials. In this article, I describe a statistical method for source data verification whose implementation can have a large impact on the workload of trial monitors. I investigate the consequences of a less stringent form of source data verification on the quality of data and the inferences that can be drawn from the data. Keywords source data verification, statistical sampling, data quality, cost savings, efficiency
1. Introduction The search for efficiency in drug development is important to pharmaceutical companies, governments, regulatory authorities, and patients. Efficiency is important for pharmaceutical companies because they are interested in increasing the return on investment and their share price, as can be read in almost any company annual report over the last 10 years. Efficiency is important to governments because they have an interest in the economic welfare of pharmaceutical companies and the health of their citizens, hence, the EU’s Innovative Medicines Initiative.1 Efficiency is important for regulatory authorities because they recognize that the regulatory drug development process is becoming ‘‘increasingly challenging, inefficient, and costly.’’2 Finally, efficiency is important for patients who expect new medicines for which they are not necessarily prepared to pay vastly increased prices. This search for efficiency has been conducted in different ways. Companies have looked to re-engineer the drug development process and have recognized the difference between the ‘‘learning’’ and ‘‘confirming’’ stages identified by Sheiner.3 Statisticians have argued for an increasing use of innovative, in particular adaptive, designs, and these ideas have been taken up by companies4 and have been recognized by governments1 and regulatory authorities.2 Basic science has a role to play, and there has been increasing use of biomarkers to expedite earlyphase (‘‘learning’’) drug development in combination with innovative designs.5
All of these have a role to play and will contribute to increased efficiency, but by how much? Beltangady and Brown report that Pfizer’s Enhanced Clinical Trial Design initiative was aimed at reducing direct grant costs by US$100 million per year.4 This is a large amount and is clearly worth saving, but there are simpler ways that can achieve at least as much. The results of a survey of members of LIF, the Swedish trade association for the pharmaceutica
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